Istituto Italiano di Tecnologia

About: Istituto Italiano di Tecnologia is a facility organization based out in Genoa, Italy. It is known for research contribution in the topics: Robot & Humanoid robot. The organization has 4561 authors who have published 14595 publications receiving 437558 citations. The organization is also known as: Italian Institute of Technology & IIT.

Multi-wavelength Raman scattering of nanostructured Al-doped zinc oxide

Abstract: In this work we present a detailed Raman scattering investigation of zinc oxide and aluminum-doped zinc oxide (AZO) films characterized by a variety of nanoscale structures and morphologies and synthesized by pulsed laser deposition under different oxygen pressure conditions. The comparison of Raman spectra for pure ZnO and AZO films with similar morphology at the nano/mesoscale allows to investigate the relation between Raman features (peak or band positions, width, relative intensity) and material properties such as local structural order, stoichiometry, and doping. Moreover Raman measurements with three different excitation lines (532, 457, and 325 nm) point out a strong correlation between vibrational and electronic properties. This observation confirms the relevance of a multi-wavelength Raman investigation to obtain a complete structural characterization of advanced doped oxide materials.

Ultra-thin conductive free-standing PEDOT/PSS nanofilms

Abstract: Free-standing conductive ultra-thin films based on poly(3,4-ethylenedioxythiophene)/poly(styrenesulfonate) (PEDOT/PSS) are realized. A fabrication process based on a modified Supporting Layer technique is proposed that provides for the easy production of conductive nanofilms having a very large surface area with typical thickness of tens of nanometres. The proposed free-standing nanofilms can be manipulated, folded and unfolded in water many times without suffering from cracks, disaggregation or from loss of conductive properties. After collecting them onto rigid or soft substrates, they retain their functionality. Structural and functional properties of the nanofilms are described by means of their thickness, topography, conductivity and Young's modulus. Strong dependences of these properties on residual water, post-deposition treatments and environmental moisture are clearly evidenced. Possible applications are foreseen in the field of sensing and actuation, as well as in the biomedical field, e.g. as smart substrates for cell culturing and stimulation.

Fractalkine receptor deficiency impairs microglial and neuronal responsiveness to chronic stress

Abstract: Chronic stress is one of the most relevant triggering factors for major depression. Microglial cells are highly sensitive to stress and, more generally, to environmental challenges. However, the role of these brain immune cells in mediating the effects of stress is still unclear. Fractalkine signaling – which comprises the chemokine CX 3 CL1, mainly expressed by neurons, and its receptor CX 3 CR1, almost exclusively present on microglia in the healthy brain – has been reported to critically regulate microglial activity. Here, we investigated whether interfering with microglial function by deleting the Cx 3 cr1 gene affects the brain’s response to chronic stress. To this purpose, we housed Cx 3 cr1 knockout and wild-type adult mice in either control or stressful environments for 2 weeks, and investigated the consequences on microglial phenotype and interactions with synapses, synaptic transmission, behavioral response and corticosterone levels. Our results show that hampering neuron–microglia communication via the CX 3 CR1–CX 3 CL1 pathway prevents the effects of chronic unpredictable stress on microglial function, short- and long-term neuronal plasticity and depressive-like behavior. Overall, the present findings suggest that microglia-regulated mechanisms may underlie the differential susceptibility to stress and consequently the vulnerability to diseases triggered by the experience of stressful events, such as major depression.

The benefit of multitask representation learning

Abstract: We discuss a general method to learn data representations from multiple tasks. We provide a justification for this method in both settings of multitask learning and learning-to-learn. The method is illustrated in detail in the special case of linear feature learning. Conditions on the theoretical advantage offered by multitask representation learning over independent task learning are established. In particular, focusing on the important example of half-space learning, we derive the regime in which multitask representation learning is beneficial over independent task learning, as a function of the sample size, the number of tasks and the intrinsic data dimensionality. Other potential applications of our results include multitask feature learning in reproducing kernel Hilbert spaces and multilayer, deep networks.

Formation of Surface Defects Dominates Ion Migration in Lead-Halide Perovskites

Abstract: We propose a new model of defect formation and ion migration at the surfaces/grain boundaries of lead-halide perovskites, based on ab initio calculations. Inspired by the spread of experimentally m...